The Effectiveness of Dohsa Psycho-Motor Rehabilitation Method on Fatigue Severity, Sleep Quality, and Resilience Promotion of Patients with Multiple Sclerosis (MS)
Bibliographic record
Abstract
Because of multiple psychological-physical symptoms and failure to accept the reality, multiple sclerosis (MS), patients are suffering from negative mood disorders and fatigue which affects their life quality negatively. Therefore this study has been conducted to determine the effect of Dohsa Psycho-Motor Rehabilitation Method on fatigue severity, sleep quality, and Resilience promotion of Patients with Multiple Sclerosis in Isfahan, Iran. A quasi-experimental study with pre-test, post-test and follow up was administered on both the experimental and control groups. The population consisted of all patients diagnosed with multiple sclerosis in Isfahan with clinical and MS society records. By purposive sampling 30 patients were selected for the experimental (n=15) and control (n=15) groups. Patients completed fatigue (FSS), Scale of sleep quality (PSQI) and Resilience Scale (CD-RISC) questionnaire before the beginning of the treatment (pretest) and also later for post-test. Dohsa treatment duration was ten sessions, three sessions per week and their post test was administered 30 days later. Finally, data were analyzed using SPSS18. The results of the multivariable covariance analysis showed that Dohsa Psycho-Motor Rehabilitation Method decreases fatigue severity, increases quality of sleep, and resilience of patients with Multiple Sclerosis (p<0.001). Since MS disease has led to widespread symptoms and different clinical signs, MS patients may need psychological rehabilitation in the future, therefore Dohsa Psycho-Motor Rehabilitation Method is an effective treatment for reducing fatigue, improving sleep quality and increasing the resilience of multiple sclerosis patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".